Perceptual audio features for unsupervised key-phrase detection

We propose a new type of audio feature (HFCC-ENS) as well as an unsupervised method for detecting short sequences of spoken words (key-phrases) within long speech recordings. Our technical contributions are threefold: Firstly, we propose to use bandwidth-adapted filterbanks instead of classical MFCC-style filters in the feature extraction step. Secondly, the time resolution of the resulting features is adapted to account for the temporal characteristics of the spoken phrases. Thirdly, the key-phrase detection step is performed by matching sequences of the resulting HFCC-ENS features with features extracted from a target speech recording. We evaluate the proposed method using the German Kiel Corpus and furthermore investigate speech-related properties of the proposed feature.